NBA 2023-24: Players with the Highest and Lowest Impact

Plus/Minus measures point differential when a player is on the court (min. 15 mins/game, 40+ games)

30DayChartChallenge
Data Visualization
R Programming
2025
Visualizing the NBA players with the greatest positive and negative impacts on their teams during the 2023-24 season using Plus/Minus statistics. Day 9 of the #30DayChartChallenge focusing on distribution and diverging charts.
Author

Steven Ponce

Published

April 9, 2025

Figure 1: A diverging bar chart titled “NBA 2023-24: Players with the Highest and Lowest Impact” showing Plus/Minus statistics for NBA players. The horizontal bars extend right (green) for positive impact and left (red) for negative impact. Nikola Jokić leads with +682, while Miles Bridges has the lowest at -633. The visualization includes 50 players who played minimum 15 minutes per game for at least 40 games.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
tidyverse,      # Easily Install and Load the 'Tidyverse'
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  janitor,        # Simple Tools for Examining and Cleaning Dirty Data
  skimr,          # Compact and Flexible Summaries of Data
  scales,         # Scale Functions for Visualization
  lubridate,      # Make Dealing with Dates a Little Easier
  hoopR,          # Access Men's Basketball Play by Play Data
  camcorder       # Record Your Plot History
)
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    height = 10,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
# Get NBA player stats for 2023-24 season
players_2024_raw <- nba_leaguedashplayerstats(season = "2023-24")
players_2024 <- players_2024_raw$LeagueDashPlayerStats

3. Examine the Data

Show code
glimpse(players_2024)
skim(players_2024)

4. Tidy Data

Show code
### |- Tidy ----
players_2024 <- players_2024 |>
  mutate(
    PLUS_MINUS = as.numeric(PLUS_MINUS),
    GP = as.numeric(GP),
    MIN = as.numeric(MIN)
  ) 

# Players with with at least 15 minutes per game and 40 games
filtered_players <- players_2024 |>
  filter(MIN >= 15, GP >= 40) 

# plot df
plot_data <- filtered_players |>
  select(PLAYER_NAME, TEAM_ABBREVIATION, GP, MIN, PLUS_MINUS) |>
  arrange(desc(PLUS_MINUS)) |> 
  # Get top and bottom 25 players by PLUS_MINUS
  mutate(rank_group = case_when(
    row_number() <= 25 ~ "Top 25",
    row_number() > n() - 25 ~ "Bottom 25",
    TRUE ~ "Middle"
  )) |>
  filter(rank_group != "Middle") |>
  # Add player names with team abbreviation
  mutate(
    player_label = str_glue("{ PLAYER_NAME } ({ TEAM_ABBREVIATION } )"),
    player_label = fct_reorder(player_label, PLUS_MINUS),
    highlight = ifelse(PLUS_MINUS > 0, "Positive Impact", "Negative Impact")
    )

# labels df
top_5 <- plot_data |> 
  arrange(desc(PLUS_MINUS)) |> 
  head(5)

bottom_5 <- plot_data |> 
  arrange(PLUS_MINUS) |> 
  head(5)

label_data <- bind_rows(top_5, bottom_5)

5. Visualization Parameters

Show code
### |- plot aesthetics ---- 
colors <- get_theme_colors(
  palette = c("Negative Impact" = "#D74A49", "Positive Impact" = "#3F9852")
  )

### |-  titles and caption ----
# text
title_text    <- str_glue("NBA 2023-24: Players with the Highest and Lowest Impact") 
subtitle_text <- str_wrap("Plus/Minus measures point differential when a player is on the court (min. 15 mins/game, 40+ games)", 
                          width = 70)

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 09,
  source_text =  "ESPN via { hoopR } package" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling 
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title = element_text(color = colors$text, size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    axis.text.y = element_text(color = colors$text, size = rel(0.68)),
    
    axis.line.x = element_line(color = "gray50", linewidth = .2),

    # Grid elements
    panel.grid.minor.x = element_blank(),
    panel.grid.major.y = element_blank(),
 
    # Legend elements
    legend.position = "top",
    legend.title = element_text(family = fonts$text, size = rel(0.8)),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),

    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot ----
p <- ggplot(
  data = plot_data,
  aes(x = player_label, y = PLUS_MINUS, fill = highlight)) +
  # Geoms
  geom_hline(yintercept = 0, color = "gray40", linetype = "solid", linewidth = 0.5) +
  geom_col() +
  geom_text(
    data = label_data,
    aes(label = round(PLUS_MINUS, 0), 
        hjust = ifelse(PLUS_MINUS > 0, -0.3, 1.3)),
    color = "black", 
    size = 3
  ) +
  # Scales
  scale_fill_manual(
    values = colors$palette,
    name = "Player Impact"
  ) +
  scale_y_continuous(
    name = "Plus/Minus",
    labels = function(x) paste0(ifelse(x > 0, "+", ""), x),
    breaks = seq(-600, 600, 300),
    limits = c(-640, 690)
  ) +
  coord_flip() +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.55),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.95),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code
### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 09, 
  width = 8, 
  height = 10
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      camcorder_0.1.0 hoopR_2.1.0     scales_1.3.0   
 [5] skimr_2.1.5     janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1    farver_2.1.2        fastmap_1.2.0      
 [4] pacman_0.5.1        promises_1.3.0      digest_0.6.37      
 [7] timechange_0.3.0    lifecycle_1.0.4     rsvg_2.6.1         
[10] processx_3.8.4      magrittr_2.0.3      compiler_4.4.0     
[13] rlang_1.1.4         tools_4.4.0         utf8_1.2.4         
[16] yaml_2.3.10         data.table_1.16.2   knitr_1.49         
[19] htmlwidgets_1.6.4   curl_6.0.0          xml2_1.3.6         
[22] repr_1.1.7          websocket_1.4.2     withr_3.0.2        
[25] grid_4.4.0          fansi_1.0.6         colorspace_2.1-1   
[28] future_1.34.0       globals_0.16.3      cli_3.6.3          
[31] rmarkdown_2.29      ragg_1.3.3          generics_0.1.3     
[34] RcppParallel_5.1.10 rstudioapi_0.17.1   httr_1.4.7         
[37] tzdb_0.4.0          commonmark_1.9.2    chromote_0.4.0     
[40] rvest_1.0.4         parallel_4.4.0      base64enc_0.1-3    
[43] vctrs_0.6.5         jsonlite_1.8.9      hms_1.1.3          
[46] listenv_0.9.1       systemfonts_1.1.0   magick_2.8.5       
[49] glue_1.8.0          parallelly_1.43.0   gifski_1.32.0-1    
[52] codetools_0.2-20    ps_1.8.1            stringi_1.8.4      
[55] gtable_0.3.6        later_1.3.2         munsell_0.5.1      
[58] furrr_0.3.1         pillar_1.9.0        htmltools_0.5.8.1  
[61] R6_2.5.1            textshaping_0.4.0   rprojroot_2.0.4    
[64] evaluate_1.0.1      markdown_1.13       gridtext_0.1.5     
[67] snakecase_0.11.1    renv_1.0.3          Rcpp_1.0.13-1      
[70] svglite_2.1.3       xfun_0.49           pkgconfig_2.0.3

9. GitHub Repository

The complete code for this analysis is available in 30dcc_2025_09.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
    • ESPN via { hoopR } package: hoopR
Back to top